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in ecological or species distribution modelling. Prospective candidates should hold a PhD in Mathematics or be in the final stages of writing up their PhD thesis and have submitted by the start date
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equations into AI-based models to solve fluid sensing problems in a robust and efficient manner. Your role may include developing new optimization techniques, coding new algorithms, creating new mathematical
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models at many scales including electron dynamics, soft-matter physics, materials-biomolecules interaction and device physics exploiting the collaboration with a multidisciplinary team of theoreticians and
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coding ability. Research Associate: Hold a PhD in Engineering, Mathematics or a closely related discipline, or equivalent research, industrial or commercial experience. *Candidates who have not yet been
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mathematical training is essential, particularly the ability to understand and verify mathematical proofs. A clear motivation to pursue a PhD or an academic career in finance or economics is also required. Our
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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, engineers, and the general public Qualifications Research Assistant Degree in engineering or numerate subject (e.g., mathematics, physical sciences, computer science) PhD close to completion in field of Power
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genetics in the grasses, especially in the model systems Zea mays (maize) or Brachypodium distachyon. We particularly welcome candidates with expertise in grass transformation and/or spatial transcriptomics
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research activities in stormwater management. The research is both theoretical and experimental with elements of computational technology and mathematical modelling and is based on close collaboration with
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. Programming gene circuits Modeling and designing synthetic DNA components Construction of Chemical Reaction Networks (CRNs) Simulation and analysis using MATLAB and Visual DSD Robust analysis of various modules